Robust Feature Space Optimization and Noise Removal from PCG

In this book, we have proposed a novel noise removal method of PCG signals using robust feature space optimization. The proposed method identifies the noisy PCG signals and eliminates them to ensure effective clinical analytics. We depict more than 85% accuracy and high specificity of identifying noisy PCG signals while experimenting over annotated PCG datasets from large publicly available MIT-Physionet database. We have also shown that propose noise removal has the capability to significantly improve the clinical utility like detection of cardiac normal/ abnormal condition from heart sound or PCG signals.